plot.ensemble {randomSurvivalForest}R Documentation

Plot Ensemble Estimates

Description

Plot ensemble survival curves and ensemble estimates of mortality.

Usage

    plot.ensemble(x, plots.one.page = TRUE, ...)

Arguments

x

An object of class (rsf, grow) or (rsf, predict).

plots.one.page

Should plots be placed on one page?

...

Further arguments passed to or from other methods.

Details

Four plots are produced. Going from top to bottom, left to right: (1) Ensemble survival function for each individual. Thick red line is overall ensemble survival, thick green line is Nelson-Aalen estimator. (2) Comparison of the population ensemble survival function to the Nelson-Aalen estimator. (3) Brier score (0=perfect, 1=poor, and 0.25=guessing) stratified by ensemble mortality. Based on the method described in Gerds et al. (2008) in which the censoring distribution is estimated using the Kaplan-Meier estimator. Stratification is into 4 groups corresponding to the 0-25, 25-50, 50-75 and 75-100 percentile values of mortality. Red line is the overall (non-stratified) Brier score. (4) Plot of mortality versus observed time. Points in blue correspond to events, black points are censored observations.

Note that when x is of class (rsf, predict) not all plots will be produced.

Applies to competing risk analyses but plots are non-event specific. Use competing.risk for event-specific curves and for a more comprehensive analysis in such cases.

Whenever possible, out-of-bag (OOB) values are used.

Author(s)

Hemant Ishwaran hemant.ishwaran@gmail.com

Udaya B. Kogalur kogalurshear@gmail.com

References

Gerds T.A., Cai T. and Schumacher M. (2008). The performance of risk prediction models, Biometrical J., 4:457-479.

Graf E., Schmoor C., Sauerbrei W. and Schumacher M. (1999). Assessment and comparison of prognostic classification schemes for survival data, Statist. in Med., 18:2529-2545.

See Also

rsf, predict.rsf.

Examples

data(veteran, package = "randomSurvivalForest") 
v.out <- rsf(Surv(time, status) ~ ., veteran, ntree = 1000)
plot.ensemble(v.out)

# plot of ensemble survival for a single individual
surv.ensb <- t(exp(-v.out$oob.ensemble))
plot(v.out$timeInterest, surv.ensb[, 1])

[Package randomSurvivalForest version 3.6.3 Index]